Welcome from the ACM IMX 2020 chairs
Bibliographic record
Abstract
It is our great pleasure to welcome you to ACM IMX 2020, the leading international conference for presentation and discussion of research into Interactive Media eXperiences.Continuing the successful tradition of the TVX series, ACM IMX brings together international researchers and practitioners from a wide range of disciplines, ranging from human-computer interaction, multimedia engineering and design, to media studies, media psychology and sociology.The conference this year has experienced major transitions and changes.The ACM International Conference on Interactive Experiences for Television and Online Video (2014-2019) is, since the 2020 edition, the ACM International Conference on Interactive Media Experiences (ACM IMX).This change better reflects the topics of interest of the community, including all types of media-based experiences.In addition, due to the COVID-19 pandemic, this year the conference is fully virtual.We are disappointed that you do not get to enjoy the beauty, culture, and culinary delights of Barcelona (hosted by i2CAT).Nevertheless, the conference organisation has worked around the clock enabling a digital ecosystem allowing for the fruitful and robust exchange of ideas, critiques, and the roll out of exciting possibilities for our field moving forwards.As a community, we will make the best of this terribly difficult time by virtually celebrating the achievements of our colleagues in pushing the field of immersive media experiences forward in impactful and meaningful ways.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.021 | 0.009 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.012 | 0.015 |
| Insufficient payload (model declined to judge) | 0.245 | 0.224 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".